Enhancing sense of belonging and satisfaction among online students in multi-track public affairs programs: A case analysis of immersion courses

Many graduate public affairs programs offer both residential and online options for students. One of the challenges for multi-format programs is creating a sense of belonging among online students who may never set foot on campus. In 2017, the MPA program at the University of North Carolina at Chapel Hill developed an ?immersion? course designed for residential and online students in a weekend intensive format on campus to help create a greater sense of connectedness and satisfaction among (principally) online students, while benefiting students in both formats. This paper examines immersion courses as one strategy to address gaps in belonging and satisfaction between online and on-campus students. The case study of UNC-Chapel Hill developing the immersion course and the first three iterations of it are described, offering practical insight for other campus-based public affairs programs that also have online degrees who may want to try something similar

Bayesian source detection and parameter estimation of a plume model based on sensor network measurements

We consider a network of sensors that measure the intensities of a complex plume composed of multiple absorption–diffusion source components. We address the problem of estimating the plume parameters, including the spatial and temporal source origins and the parameters of the diffusion model for each source, based on a sequence of sensor measurements. The approach not only leads to multiple-source detection, but also the characterization and prediction of the combined plume in space and time. The parameter estimation is formulated as a Bayesian inference problem, and the solution is obtained using a Markov chain Monte Carlo algorithm. The approach is applied to a simulation study, which shows that an accurate parameter estimation is achievable. Copyright © 2010 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/78051/1/859_ftp.pd

AlzPharm: integration of neurodegeneration data using RDF

<p>Abstract</p> <p>Background</p> <p>Neuroscientists often need to access a wide range of data sets distributed over the Internet. These data sets, however, are typically neither integrated nor interoperable, resulting in a barrier to answering complex neuroscience research questions. Domain ontologies can enable the querying heterogeneous data sets, but they are not sufficient for neuroscience since the data of interest commonly span multiple research domains. To this end, e-Neuroscience seeks to provide an integrated platform for neuroscientists to discover new knowledge through seamless integration of the very diverse types of neuroscience data. Here we present a Semantic Web approach to building this e-Neuroscience framework by using the Resource Description Framework (RDF) and its vocabulary description language, RDF Schema (RDFS), as a standard data model to facilitate both representation and integration of the data.</p> <p>Results</p> <p>We have constructed a pilot ontology for BrainPharm (a subset of SenseLab) using RDFS and then converted a subset of the BrainPharm data into RDF according to the ontological structure. We have also integrated the converted BrainPharm data with existing RDF hypothesis and publication data from a pilot version of SWAN (Semantic Web Applications in Neuromedicine). Our implementation uses the RDF Data Model in Oracle Database 10g release 2 for data integration, query, and inference, while our Web interface allows users to query the data and retrieve the results in a convenient fashion.</p> <p>Conclusion</p> <p>Accessing and integrating biomedical data which cuts across multiple disciplines will be increasingly indispensable and beneficial to neuroscience researchers. The Semantic Web approach we undertook has demonstrated a promising way to semantically integrate data sets created independently. It also shows how advanced queries and inferences can be performed over the integrated data, which are hard to achieve using traditional data integration approaches. Our pilot results suggest that our Semantic Web approach is suitable for realizing e-Neuroscience and generic enough to be applied in other biomedical fields.</p

Approximate Analytical Solutions of the Pseudospin Symmetric Dirac Equation for Exponential-type Potentials

The solvability of The Dirac equation is studied for the exponential-type potentials with the pseudospin symmetry by using the parametric generalization of the Nikiforov-Uvarov method. The energy eigenvalue equation, and the corresponding Dirac spinors for Morse, Hulthen, and q-deformed Rosen-Morse potentials are obtained within the framework of an approximation to the spin-orbit coupling term, so the solutions are given for any value of the spin-orbit quantum number $\kappa=0$, or $\kappa \neq 0$.Comment: 16 page

Collective decision making and social interaction rules in mixed-species flocks of songbirds

Associations in mixed-species foraging groups are common in animals, yet have rarely been explored in the context of collective behaviour. Despite many investigations into the social and ecological conditions under which individuals should form groups, we still know little about the specific behavioural rules that individuals adopt in these contexts, or whether these can be generalized to heterospecifics. Here, we studied collective behaviour in flocks in a community of five species of woodland passerine birds. We adopted an automated data collection protocol, involving visits by RFID-tagged birds to feeding stations equipped with antennae, over two winters, recording 91 576 feeding events by 1904 individuals. We demonstrated highly synchronized feeding behaviour within patches, with birds moving towards areas of the patch with the largest proportion of the flock. Using a model of collective decision making, we then explored the underlying decision rule birds may be using when foraging in mixed-species flocks. The model tested whether birds used a different decision rule for conspecifics and heterospecifics, and whether the rules used by individuals of different species varied. We found that species differed in their response to the distribution of conspecifics and heterospecifics across foraging patches. However, simulating decisions using the different rules, which reproduced our data well, suggested that the outcome of using different decision rules by each species resulted in qualitatively similar overall patterns of movement. It is possible that the decision rules each species uses may be adjusted to variation in mean species abundance in order for individuals to maintain the same overall flock-level response. This is likely to be important for maintaining coordinated behaviour across species, and to result in quick and adaptive flock responses to food resources that are patchily distributed in space and time

Quantifying trends in disease impact to produce a consistent and reproducible definition of an emerging infectious disease.

The proper allocation of public health resources for research and control requires quantification of both a disease's current burden and the trend in its impact. Infectious diseases that have been labeled as "emerging infectious diseases" (EIDs) have received heightened scientific and public attention and resources. However, the label 'emerging' is rarely backed by quantitative analysis and is often used subjectively. This can lead to over-allocation of resources to diseases that are incorrectly labelled "emerging," and insufficient allocation of resources to diseases for which evidence of an increasing or high sustained impact is strong. We suggest a simple quantitative approach, segmented regression, to characterize the trends and emergence of diseases. Segmented regression identifies one or more trends in a time series and determines the most statistically parsimonious split(s) (or joinpoints) in the time series. These joinpoints in the time series indicate time points when a change in trend occurred and may identify periods in which drivers of disease impact change. We illustrate the method by analyzing temporal patterns in incidence data for twelve diseases. This approach provides a way to classify a disease as currently emerging, re-emerging, receding, or stable based on temporal trends, as well as to pinpoint the time when the change in these trends happened. We argue that quantitative approaches to defining emergence based on the trend in impact of a disease can, with appropriate context, be used to prioritize resources for research and control. Implementing this more rigorous definition of an EID will require buy-in and enforcement from scientists, policy makers, peer reviewers and journal editors, but has the potential to improve resource allocation for global health

Calibration and Characterization of the IceCube Photomultiplier Tube

Over 5,000 PMTs are being deployed at the South Pole to compose the IceCube neutrino observatory. Many are placed deep in the ice to detect Cherenkov light emitted by the products of high-energy neutrino interactions, and others are frozen into tanks on the surface to detect particles from atmospheric cosmic ray showers. IceCube is using the 10-inch diameter R7081-02 made by Hamamatsu Photonics. This paper describes the laboratory characterization and calibration of these PMTs before deployment. PMTs were illuminated with pulses ranging from single photons to saturation level. Parameterizations are given for the single photoelectron charge spectrum and the saturation behavior. Time resolution, late pulses and afterpulses are characterized. Because the PMTs are relatively large, the cathode sensitivity uniformity was measured. The absolute photon detection efficiency was calibrated using Rayleigh-scattered photons from a nitrogen laser. Measured characteristics are discussed in the context of their relevance to IceCube event reconstruction and simulation efforts.Comment: 40 pages, 12 figure

Are specialists at risk under environmental change? Neoecological, paleoecological and phylogenetic approaches

The question ‘what renders a species extinction prone’ is crucial to biologists. Ecological specialization has been suggested as a major constraint impeding the response of species to environmental changes. Most neoecological studies indicate that specialists suffer declines under recent environmental changes. This was confirmed by many paleoecological studies investigating longer-term survival. However, phylogeneticists, studying the entire histories of lineages, showed that specialists are not trapped in evolutionary dead ends and could even give rise to generalists. Conclusions from these approaches diverge possibly because (i) of approach-specific biases, such as lack of standardization for sampling efforts (neoecology), lack of direct observations of specialization (paleoecology), or binary coding and prevalence of specialists (phylogenetics); (ii) neoecologists focus on habitat specialization; (iii) neoecologists focus on extinction of populations, phylogeneticists on persistence of entire clades through periods of varying extinction and speciation rates; (iv) many phylogeneticists study species in which specialization may result from a lack of constraints. We recommend integrating the three approaches by studying common datasets, and accounting for range-size variation among species, and we suggest novel hypotheses on why certain specialists may not be particularly at risk and consequently why certain generalists deserve no less attention from conservationists than specialists